Tracking of multidrug-resistant pathogen clones in Ghana: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Antimicrobial resistance (AMR) threatens effective antibiotic treatment. Multidrug-resistant (MDR) bacterial clones pose a particular challenge because they facilitate rapid resistance dissemination. Tracking dominant antibiotic-resistant clones in Ghana will inform targeted surveillance and control. This systematic review characterised prevalent MDR clones of priority pathogens isolated from humans, animals, and the environment in Ghana. METHODS: A search of PubMed, Scopus, and Web of Science databases was conducted from inception to October 4, 2024, for studies reporting genetic characterisation of MDR clones from Ghana. The risk of bias in the included studies was evaluated using the Newcastle–Ottawa scale (NOS), and data analysis involved descriptive statistics and proportional meta-analysis. RESULTS: Twenty-five studies met the eligibility criteria, and 10 different MDR bacterial species were identified from human, animal, and environmental sources. The pooled prevalence of MDR bacteria was 53.4% (95% CI: 39.8–66.9). The dominant E. coli clones were ST155 (38.0%), ST617 (29.1%), and ST10 (11.1%). For K. pneumoniae, ST152 and ST17 were the main clones detected, each with a prevalence of 13.7%. ST39 was also present at 9.0%. The major S. pneumoniae clones were ST802 (18.5%), ST15111 (12.3%), and ST15448 (4.8%). ST152 (27.3%), ST121 (21.3%), and ST9 (14.3%) were predominant among the S. aureus isolates. The most prevalent Acinetobacter baumannii clone was ST231 (77.3%), followed by ST2145 (13.6%). CONCLUSION: This systematic review provided the first comprehensive overview of MDR clones that may be circulating in Ghana. The identification of high-risk clones, such as E. coli ST155 and S. aureus ST152, highlights the need for urgent public health interventions. Continued tracking using standardised WGS methodologies across diverse sources is crucial for guiding antimicrobial resistance containment in Ghana.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.024 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".